.autolab/acquired_skills/comparative-genomics/SKILL.md
# Comparative Genomics Skill ## When to Use Use for comparing gene content, synteny, and functional modules across genomes. ## Standard Workflow 1. Predict genes with Prodigal 2. Identify orthologous clusters with OrthoFinder or similar 3. Annotate clusters with functional databases (COG, KEGG, etc.) 4. Filter for conserved gene clusters present across all genomes 5. Identify co-evolving gene modules ## Key Decisions - Use KEGG/COG annotations for functional characterization - Filter for clus
npx skillsauth add albert-ying/autonomous-lab .autolab/acquired_skills/comparative-genomicsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use for comparing gene content, synteny, and functional modules across genomes.
development
Critically review AI-agent-conducted scientific analyses for correctness, rigor, and completeness. Use this skill whenever an analysis session has completed and needs validation, when a user asks to "review," "validate," "check," or "audit" a computational analysis, or when an agent pipeline produces scientific results that require quality control before reporting. Also trigger when the user references an execution trace, notebook, or conversation history from a prior analysis session. This skill should run as the final step of any autonomous scientific analysis pipeline.
tools
# Variant Calling Skill ## When to Use Use when calling SNPs and indels from aligned BAM files against a reference. ## Standard Workflow 1. Mark duplicates (optional): `samtools markdup` 2. Call variants with freebayes: `freebayes -f reference.fasta -p 1 sample.bam > variants.vcf` OR with bcftools: `bcftools mpileup -f ref.fa sample.bam | bcftools call -mv -Oz -o variants.vcf.gz` 3. Filter variants: `bcftools filter -s LowQual -e 'QUAL<20' variants.vcf` ## Key Decisions - For haploid organ
tools
# Trimmomatic - Read Quality Trimming ## When to Use Use Trimmomatic to trim adapter sequences and low-quality bases from Illumina sequencing reads. ## Standard Workflow 1. Install: `conda install -c bioconda trimmomatic` 2. Run: `trimmomatic PE <input_R1.fastq.gz> <input_R2.fastq.gz> <output_R1_paired.fastq.gz> <output_R1_unpaired.fastq.gz> <output_R2_paired.fastq.gz> <output_R2_unpaired.fastq.gz> ILLUMINACLIP:<adapters.fa>:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36` ## Key Pa
testing
# SPAdes Assembly Skill ## When to Use Use for de novo genome assembly when no reference genome is available. ## Standard Workflow 1. Run SPAdes: `spades.py -1 R1.fastq.gz -2 R2.fastq.gz -o assembly_output --careful` 2. Check assembly stats: look at scaffolds.fasta or contigs.fasta 3. Use assembled genome as reference for read mapping ## Key Decisions - Use `--careful` flag for bacterial genomes to reduce misassemblies - For small bacterial genomes, default k-mer sizes work well - Output scaf